wg1.0.e == “western gesture 1, condition 0, eastern viewer culture” eg4.2.w == “eastern gesture 4 condition 2, western viewer culture”
Load these bad larrys (straight from mTurk template)
Also load these buggerinos (from psytoolkit template)
some things should correlate, others should not. IE the questions that go together should correlate. accessible <–> open confident <–> sure conflict <–> tension dominant <–> control goal <–> worktogether many <–> members
So now we group the metaphor measures by group and facet wrap by that. EZ
Create grouped overlay of correlated questions. Violin plot of question correlations by group (for single conditions of a single gesture)
This graph shows the correlation of the grouped variables. We want them to be highly correlated with each other, and preferably not super correlated with one another. This should hold moreso for the extreme versions of the gestures.
Really could have correlation plot w Q1 on X and Q2 on Y but those don’t look great and we shouldn’t expect them to…
Get wrapped plot of correlations across conditions for same gesture
Regrouping data in a potentially horrifying way. Need to make sure questions make sense…
Group it all together and do it by group but you can also just do it by groups and facet wrap by metaphor measure for indiv question results.
## Saving 7 x 5 in image
Western Gestures, Western/Eastern Participants
Eastern Gestures, Western/Eastern Participants
To get the individual ones (per gesture, say) you can do this to position the w/e side by side.
Cool now access everything as follows: all_dat$overlays[["d0_overlay"]]: the overlayed violin plot of related questions. Illustrates density overlay aka a nice vis of correlation of questions
all_dat$correlation_matrix: the correlation matrices that visualize the above as well.
all_dat$violin_density_question: the violin plot of all question distributions across gesture conditions.
all_dat$violin_density_grouped_overlay: the violin plot of all question distributions across gesture conditions, but overlayed.
all_dat$violin_density_grouped: the violin plot of group distributions across gesture conditions.
all_dat$density_grouped: density plot of group distributions across gesture conditions.
all_dat$density_question: density plot of all question distributions across gesture conditions.
if you want to plot all of the overlays nicely you can do this:
Pretty, but what does it mean? Well, to determine whether any of these differences are significant (aka, did people interpret different things from each of the different gesture conditions, which, because we, too, are people, we know they did) we need to see what the significant differences between rankings in each gesture and condition are.
Now get those T-Tests done DID.
| group | cond1 | cond2 | p | sig |
|---|---|---|---|---|
| openness | cond2 | cond1 | 0.05197 | * |
| openness | original | cond2 | 0.08087 | * |
| conflict | cond2 | cond1 | 0.00000 | *** |
| conflict | original | cond2 | 0.00000 | *** |
| unity | cond2 | cond1 | 0.00000 | *** |
| unity | original | cond2 | 0.00011 | *** |
| group | cond1 | cond2 | p | sig |
|---|---|---|---|---|
| conflict | cond1 | original | 0.00001 | *** |
| conflict | cond2 | cond1 | 0.06043 | * |
| control | cond1 | original | 0.09701 | * |
| group | cond1 | cond2 | p | sig |
|---|---|---|---|---|
| openness | cond2 | cond1 | 0.00234 | ** |
| openness | original | cond2 | 0.00406 | ** |
| control | cond2 | cond1 | 0.01833 | * |
| size | cond2 | cond1 | 0.01760 | * |
| group | cond1 | cond2 | p | sig |
|---|
| group | cond1 | cond2 | p | sig |
|---|---|---|---|---|
| conflict | original | cond2 | 0.08661 | * |
| unity | cond2 | cond1 | 0.00574 | * |
cool now we have all that data in one place. We need to compare the western and eastern viewers across conditions.
Easy enough to do stats on means between cultures but baby I wanna see those VIOLIN PLOTSSSSSS.
Can only plot by one thing at a time (i.e. for a single gesture condition then visualize across metaphor measures, or for a single metaphor measure then visualize across conditions. The second doesn’t make sense though….)
## `stat_bindot()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Computation failed in `stat_bindot()`:
## attempt to apply non-function
## Warning: Computation failed in `stat_bindot()`:
## attempt to apply non-function
## Warning: Computation failed in `stat_bindot()`:
## attempt to apply non-function
## Warning: Computation failed in `stat_bindot()`:
## attempt to apply non-function
## Warning: Computation failed in `stat_bindot()`:
## attempt to apply non-function
## Warning: Computation failed in `stat_bindot()`:
## attempt to apply non-function
Now the plots live in things like wg1_violin_culture_comparisons$c0_violin_comparison
Cool now across I actually don’t think the other comparison makes sense because you’re comparing things across different gestures which defeats the purpose of the visualizations.
So anyway,
Could do ANOVAs but it doesn’t necessarily make sense since we’re just comparing two groups.
Now all that data lives in things like wg1_total_cultural_comparison_tables$c0_comparison that look like this: WG1-0 (this name isn’t here in the actual version, we just know from the naming scheme)
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.710526 | 4.107143 | 1.00000 | |
| conflict | 3.855263 | 4.160714 | 1.00000 | |
| control | 4.210526 | 3.642857 | 0.69973 | |
| size | 4.276316 | 4.178571 | 1.00000 | |
| certainty | 4.921053 | 4.267857 | 0.13799 | |
| unity | 5.105263 | 4.535714 | 0.71207 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.900000 | 3.5000 | 1.00000 | |
| conflict | 3.133333 | 3.9375 | 0.10881 | |
| control | 4.416667 | 3.8125 | 0.29329 | |
| size | 4.216667 | 3.8375 | 1.00000 | |
| certainty | 5.166667 | 3.7750 | 0.00000 | *** |
| unity | 5.400000 | 4.2750 | 0.00039 | *** |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 2.986111 | 3.105263 | 1.00000 | |
| conflict | 5.888889 | 4.986842 | 0.00703 | * |
| control | 4.694444 | 3.934210 | 0.02556 | * |
| size | 4.291667 | 4.197368 | 1.00000 | |
| certainty | 4.625000 | 4.184210 | 1.00000 | |
| unity | 3.625000 | 4.355263 | 0.20479 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.381579 | 3.78 | 1.00000 | |
| conflict | 5.486842 | 3.94 | 0.00001 | *** |
| control | 4.618421 | 3.48 | 0.00168 | ** |
| size | 4.421053 | 4.12 | 1.00000 | |
| certainty | 4.276316 | 4.54 | 1.00000 | |
| unity | 4.263158 | 4.54 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.011905 | 2.937500 | 0.00014 | *** |
| conflict | 4.011905 | 4.921875 | 0.02176 | * |
| control | 3.892857 | 4.703125 | 0.03206 | * |
| size | 3.702381 | 3.406250 | 1.00000 | |
| certainty | 4.797619 | 4.453125 | 1.00000 | |
| unity | 4.702381 | 3.984375 | 0.03296 | * |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.657143 | 3.051282 | 0.21780 | |
| conflict | 4.957143 | 4.474359 | 1.00000 | |
| control | 4.600000 | 4.076923 | 0.69852 | |
| size | 4.171429 | 3.564103 | 0.34673 | |
| certainty | 4.957143 | 4.307692 | 0.06013 | * |
| unity | 4.928571 | 4.320513 | 0.22153 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.756757 | 3.722222 | 1.00000 | |
| conflict | 3.297297 | 3.722222 | 1.00000 | |
| control | 4.324324 | 3.666667 | 0.26782 | |
| size | 5.027027 | 4.962963 | 1.00000 | |
| certainty | 4.783784 | 4.259259 | 0.23807 | |
| unity | 5.445946 | 5.000000 | 0.79275 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.684210 | 3.500000 | 1.00000 | |
| conflict | 3.697368 | 3.833333 | 1.00000 | |
| control | 4.881579 | 3.727273 | 0.00099 | ** |
| size | 4.578947 | 4.106061 | 1.00000 | |
| certainty | 4.500000 | 4.196970 | 1.00000 | |
| unity | 4.907895 | 4.500000 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.739130 | 3.800000 | 0.00338 | ** |
| conflict | 3.119565 | 3.950000 | 0.02874 | * |
| control | 3.923913 | 3.683333 | 1.00000 | |
| size | 5.445652 | 4.833333 | 0.11437 | |
| certainty | 4.989130 | 4.233333 | 0.00460 | ** |
| unity | 5.554348 | 4.600000 | 0.00010 | *** |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.645161 | 4.7500 | 1.00000 | |
| conflict | 3.145161 | 3.3250 | 1.00000 | |
| control | 3.725807 | 3.1625 | 0.46166 | |
| size | 5.209677 | 4.6000 | 0.16060 | |
| certainty | 4.903226 | 4.8250 | 1.00000 | |
| unity | 5.306452 | 4.9500 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.659091 | 4.158537 | 0.38857 | |
| conflict | 3.545454 | 3.780488 | 1.00000 | |
| control | 4.045454 | 3.536585 | 0.68870 | |
| size | 5.431818 | 4.195122 | 0.00003 | *** |
| certainty | 4.954546 | 4.829268 | 1.00000 | |
| unity | 4.954546 | 4.646342 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.111111 | 3.783784 | 1.00000 | |
| conflict | 3.907407 | 4.067568 | 1.00000 | |
| control | 4.462963 | 3.351351 | 0.00091 | ** |
| size | 4.777778 | 4.527027 | 1.00000 | |
| certainty | 4.444444 | 4.337838 | 1.00000 | |
| unity | 4.740741 | 4.851351 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.454546 | 4.214286 | 1.00000 | |
| conflict | 2.886364 | 3.273810 | 1.00000 | |
| control | 3.318182 | 3.297619 | 1.00000 | |
| size | 4.454546 | 3.857143 | 0.47394 | |
| certainty | 4.363636 | 4.178571 | 1.00000 | |
| unity | 5.159091 | 4.857143 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.947368 | 3.9625 | 0.01141 | * |
| conflict | 3.578947 | 3.2625 | 1.00000 | |
| control | 4.157895 | 3.4875 | 0.56558 | |
| size | 4.684210 | 3.7375 | 0.00638 | * |
| certainty | 5.078947 | 3.8125 | 0.00045 | *** |
| unity | 5.894737 | 4.3375 | 0.00000 | *** |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.270833 | 4.390625 | 1.00000 | |
| conflict | 4.000000 | 3.578125 | 1.00000 | |
| control | 3.916667 | 3.656250 | 1.00000 | |
| size | 4.708333 | 4.171875 | 0.32606 | |
| certainty | 4.479167 | 4.265625 | 1.00000 | |
| unity | 4.875000 | 4.484375 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.939394 | 4.000000 | 1 | |
| conflict | 3.893939 | 3.862069 | 1 | |
| control | 3.636364 | 3.189655 | 1 | |
| size | 4.303030 | 4.275862 | 1 | |
| certainty | 4.212121 | 4.465517 | 1 | |
| unity | 4.772727 | 4.862069 | 1 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.367647 | 3.166667 | 1.00000 | |
| conflict | 4.647059 | 4.187500 | 1.00000 | |
| control | 4.911765 | 4.229167 | 0.43542 | |
| size | 4.529412 | 3.770833 | 0.04422 | * |
| certainty | 4.617647 | 4.395833 | 1.00000 | |
| unity | 4.691177 | 4.437500 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.231482 | 3.685185 | 0.56295 | |
| conflict | 4.157407 | 4.185185 | 1.00000 | |
| control | 3.712963 | 3.277778 | 1.00000 | |
| size | 4.379630 | 4.444444 | 1.00000 | |
| certainty | 3.944444 | 4.092593 | 1.00000 | |
| unity | 4.768518 | 4.648148 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.369565 | 3.785714 | 1.00000 | |
| conflict | 4.445652 | 3.964286 | 1.00000 | |
| control | 4.391304 | 3.607143 | 0.61269 | |
| size | 4.293478 | 4.214286 | 1.00000 | |
| certainty | 4.445652 | 4.357143 | 1.00000 | |
| unity | 4.684783 | 4.642857 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.978723 | 3.75000 | 1.00000 | |
| conflict | 3.255319 | 3.96875 | 0.11945 | |
| control | 3.712766 | 3.81250 | 1.00000 | |
| size | 4.893617 | 4.00000 | 0.01229 | * |
| certainty | 4.723404 | 4.00000 | 0.01770 | * |
| unity | 5.308511 | 4.68750 | 0.24359 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.441177 | 3.452381 | 1.00000 | |
| conflict | 4.686274 | 4.309524 | 1.00000 | |
| control | 4.205882 | 3.714286 | 1.00000 | |
| size | 4.666667 | 4.095238 | 0.41235 | |
| certainty | 4.098039 | 4.309524 | 1.00000 | |
| unity | 4.274510 | 4.452381 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.707317 | 3.696429 | 1.00000 | |
| conflict | 3.317073 | 4.053571 | 0.07923 | * |
| control | 4.036585 | 3.946429 | 1.00000 | |
| size | 4.707317 | 4.571429 | 1.00000 | |
| certainty | 4.963415 | 4.714286 | 1.00000 | |
| unity | 5.475610 | 4.982143 | 0.47833 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.987179 | 4.142857 | 1.0000 | |
| conflict | 4.000000 | 3.476190 | 1.0000 | |
| control | 3.794872 | 3.571429 | 1.0000 | |
| size | 4.717949 | 4.000000 | 0.1541 | |
| certainty | 4.679487 | 4.785714 | 1.0000 | |
| unity | 5.282051 | 4.880952 | 1.0000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.988095 | 3.475 | 1.00000 | |
| conflict | 3.178571 | 4.425 | 0.00254 | ** |
| control | 3.904762 | 3.300 | 1.00000 | |
| size | 4.750000 | 4.275 | 1.00000 | |
| certainty | 4.785714 | 4.150 | 0.58306 | |
| unity | 5.547619 | 4.875 | 0.22946 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.206522 | 3.578947 | 0.23434 | |
| conflict | 3.336956 | 4.157895 | 0.04647 | * |
| control | 4.184783 | 3.605263 | 0.97028 | |
| size | 5.141304 | 4.315790 | 0.01705 | * |
| certainty | 4.804348 | 4.578947 | 1.00000 | |
| unity | 5.260870 | 4.315790 | 0.01021 | * |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 4.1875 | 3.729167 | 0.78680 | |
| conflict | 3.9000 | 3.937500 | 1.00000 | |
| control | 4.1625 | 3.833333 | 1.00000 | |
| size | 5.0875 | 4.208333 | 0.01834 | * |
| certainty | 4.6125 | 4.916667 | 1.00000 | |
| unity | 4.9750 | 4.791667 | 1.00000 |
| groups | western_mean | eastern_mean | p | sig |
|---|---|---|---|---|
| openness | 3.935897 | 3.600 | 1.00000 | |
| conflict | 4.333333 | 4.000 | 1.00000 | |
| control | 4.730769 | 3.600 | 0.00316 | ** |
| size | 4.987179 | 4.425 | 0.48959 | |
| certainty | 4.717949 | 4.425 | 1.00000 | |
| unity | 4.923077 | 4.400 | 0.71649 |
Quick spot check…
Short Answer: yes because we grouped so n = 2n lol. Interestingly, only for the significant differences do we see powers > 0.8. I mean not that interestingly cause like that’s how effect size works but still. Anyway, power is definitely high enough.
Example usage:
cond_power <- calculate_power_for_condition(wg2_total, "conflict", "original")
Then you get a variable called cond_power you can use to see the actual power through cond_power$power. In this case our power is 0.9946474